Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data

By Paul Zikopoulos, C. Eaton. 2011

1,485 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/21b449c01bdc488f819204869612203c1b368795

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Citations in the corpus

Big data analytics in smart grids: a review (2018, methodology): “Therefore, the definition of big data depends on the ability of data mining algorithms and the corresponding hardware equipment to deal with large volume datasets (Zikopoulos & Eaton, 2011).”
Big data analytics in healthcare: promise and potential (2014, unknown): “Hadoop has the potential to process extremely large amounts of data mainly by allocating partitioned data sets to numerous servers (nodes), each of which solves different parts of the larger problem and then integrates them for the final result [28-31].”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, background): “It is estimated that 80% of this data is unstructured [205, 196], consisting largely of images, videos, and raw text.”
Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph (2015, methodology): “A review of literature (Cohen et al., 2009; Zikopoulos and Eaton, 2011; Huddar and Ramannavar, 2013) shows that there are various existing techniques i.e. Hadoop and MapReduce which is available to managers to harvest big data.”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, unknown): “[390] P.”
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, background): “The fourth V can be value, variability, or virtual [207].”
Mining big data: current status, and forecast to the future (2013, background): “• Apache Hadoop related projects [36]: Apache Pig, Apache Hive, Apache HBase, Apache ZooKeeper, Apache Cas-sandra, Cascading, Scribe and many others.”
Big data: A review (2013, methodology): “Semistructured data does not conform to fixed fields but contains tags to separate data elements [4,17].”
Big data classification: problems and challenges in network intrusion prediction with machine learning (2014, methodology): “Big Data is currently defined using three data characteristics: volume, variety and velocity [1].”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, methodology): “Current commercial SSD-based multi-tier systems from IBM, EMC, 3PAR and Compllent already gain satisfied performance.”
Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives (2016, methodology): “Thus, open-source data visualization tools are preferred by the end-users for free application which could be easily conjunction with the existing systems in a company (Zikopoulos & Eaton, 2011).”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, background): “, we need to deal not only with high-dimensional data but also with the variety data and stream data [338].”
Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing (2018, background): “The first V is volume in reference to the size of the data, the second V is variety in reference to the different types/formats of the data, the third V is velocity in reference to the speed of data generation, and the last V is veracity in reference to the reliability of the data [61].”
Big Data and cloud computing: innovation opportunities and challenges (2017, background): “Past research on processing Big Data focused on the distributed and stream-based processing (Zikopoulos and Eaton 2011).”